[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-lidangzzz--goal-driven":3,"tool-lidangzzz--goal-driven":61},[4,18,26,36,44,53],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":10,"last_commit_at":24,"category_tags":25,"status":17},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[14,15,13],{"id":27,"name":28,"github_repo":29,"description_zh":30,"stars":31,"difficulty_score":32,"last_commit_at":33,"category_tags":34,"status":17},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",149489,2,"2026-04-10T11:32:46",[14,13,35],"语言模型",{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":32,"last_commit_at":42,"category_tags":43,"status":17},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",108322,"2026-04-10T11:39:34",[14,15,13],{"id":45,"name":46,"github_repo":47,"description_zh":48,"stars":49,"difficulty_score":32,"last_commit_at":50,"category_tags":51,"status":17},6121,"gemini-cli","google-gemini\u002Fgemini-cli","gemini-cli 是一款由谷歌推出的开源 AI 命令行工具，它将强大的 Gemini 大模型能力直接集成到用户的终端环境中。对于习惯在命令行工作的开发者而言，它提供了一条从输入提示词到获取模型响应的最短路径，无需切换窗口即可享受智能辅助。\n\n这款工具主要解决了开发过程中频繁上下文切换的痛点，让用户能在熟悉的终端界面内直接完成代码理解、生成、调试以及自动化运维任务。无论是查询大型代码库、根据草图生成应用，还是执行复杂的 Git 操作，gemini-cli 都能通过自然语言指令高效处理。\n\n它特别适合广大软件工程师、DevOps 人员及技术研究人员使用。其核心亮点包括支持高达 100 万 token 的超长上下文窗口，具备出色的逻辑推理能力；内置 Google 搜索、文件操作及 Shell 命令执行等实用工具；更独特的是，它支持 MCP（模型上下文协议），允许用户灵活扩展自定义集成，连接如图像生成等外部能力。此外，个人谷歌账号即可享受免费的额度支持，且项目基于 Apache 2.0 协议完全开源，是提升终端工作效率的理想助手。",100752,"2026-04-10T01:20:03",[52,13,15,14],"插件",{"id":54,"name":55,"github_repo":56,"description_zh":57,"stars":58,"difficulty_score":10,"last_commit_at":59,"category_tags":60,"status":17},4487,"LLMs-from-scratch","rasbt\u002FLLMs-from-scratch","LLMs-from-scratch 是一个基于 PyTorch 的开源教育项目，旨在引导用户从零开始一步步构建一个类似 ChatGPT 的大型语言模型（LLM）。它不仅是同名技术著作的官方代码库，更提供了一套完整的实践方案，涵盖模型开发、预训练及微调的全过程。\n\n该项目主要解决了大模型领域“黑盒化”的学习痛点。许多开发者虽能调用现成模型，却难以深入理解其内部架构与训练机制。通过亲手编写每一行核心代码，用户能够透彻掌握 Transformer 架构、注意力机制等关键原理，从而真正理解大模型是如何“思考”的。此外，项目还包含了加载大型预训练权重进行微调的代码，帮助用户将理论知识延伸至实际应用。\n\nLLMs-from-scratch 特别适合希望深入底层原理的 AI 开发者、研究人员以及计算机专业的学生。对于不满足于仅使用 API，而是渴望探究模型构建细节的技术人员而言，这是极佳的学习资源。其独特的技术亮点在于“循序渐进”的教学设计：将复杂的系统工程拆解为清晰的步骤，配合详细的图表与示例，让构建一个虽小但功能完备的大模型变得触手可及。无论你是想夯实理论基础，还是为未来研发更大规模的模型做准备",90106,"2026-04-06T11:19:32",[35,15,13,14],{"id":62,"github_repo":63,"name":64,"description_en":65,"description_zh":66,"ai_summary_zh":67,"readme_en":68,"readme_zh":69,"quickstart_zh":70,"use_case_zh":71,"hero_image_url":72,"owner_login":73,"owner_name":74,"owner_avatar_url":75,"owner_bio":76,"owner_company":77,"owner_location":78,"owner_email":79,"owner_twitter":73,"owner_website":80,"owner_url":81,"languages":79,"stars":82,"forks":83,"last_commit_at":84,"license":79,"difficulty_score":85,"env_os":86,"env_gpu":87,"env_ram":87,"env_deps":88,"category_tags":91,"github_topics":79,"view_count":32,"oss_zip_url":79,"oss_zip_packed_at":79,"status":17,"created_at":92,"updated_at":93,"faqs":94,"releases":95},6212,"lidangzzz\u002Fgoal-driven","goal-driven","A multi-agent system that keeps running for ~100 hours and solve a very complicated coding or math problem that can be verified","Goal-Driven 是一套专为多智能体系统设计的协作框架，旨在让 AI 团队能够持续运行数十甚至上百小时，攻克那些逻辑极其复杂、耗时漫长但结果可严格验证的编程或数学难题。它有效解决了传统 AI 在面对大型工程任务时容易“半途而废”或过早宣布完成的痛点，通过独特的“主智能体 + 子智能体”双角色机制，确保任务执行的高韧性与准确性。\n\n在该系统中，子智能体负责埋头苦干、持续解决问题；而主智能体则扮演严格的“监工”与“裁判”，依据预设的成功标准周期性检查进度。只有当产出完全符合标准时，系统才会停止，否则将指令子智能体继续迭代，直至达标。这种机制特别适合需要长时间专注的开发者、科研人员及系统架构师，用于挑战编译器开发、数学定理证明、数据库架构设计或 EDA 仿真等高难度领域。\n\nGoal-Driven 的核心亮点在于其极简却强大的闭环逻辑：它不依赖复杂的插件，仅通过一段精心设计的提示词模板，即可激活现有工具（如 Claude Code、Codex 等）的潜能，将其转化为能独立运作超 300 小时的自动化研发引擎。已有案例成功利用该框架在约 100 小时内用 C++ 重写了 TypeScri","Goal-Driven 是一套专为多智能体系统设计的协作框架，旨在让 AI 团队能够持续运行数十甚至上百小时，攻克那些逻辑极其复杂、耗时漫长但结果可严格验证的编程或数学难题。它有效解决了传统 AI 在面对大型工程任务时容易“半途而废”或过早宣布完成的痛点，通过独特的“主智能体 + 子智能体”双角色机制，确保任务执行的高韧性与准确性。\n\n在该系统中，子智能体负责埋头苦干、持续解决问题；而主智能体则扮演严格的“监工”与“裁判”，依据预设的成功标准周期性检查进度。只有当产出完全符合标准时，系统才会停止，否则将指令子智能体继续迭代，直至达标。这种机制特别适合需要长时间专注的开发者、科研人员及系统架构师，用于挑战编译器开发、数学定理证明、数据库架构设计或 EDA 仿真等高难度领域。\n\nGoal-Driven 的核心亮点在于其极简却强大的闭环逻辑：它不依赖复杂的插件，仅通过一段精心设计的提示词模板，即可激活现有工具（如 Claude Code、Codex 等）的潜能，将其转化为能独立运作超 300 小时的自动化研发引擎。已有案例成功利用该框架在约 100 小时内用 C++ 重写了 TypeScript 编译器，或在 30 小时内用 Rust 实现了 SQLite 核心，展现了其在处理高抽象度任务上的巨大潜力。","[中文版](readme_cn.md)\n\n# Goal-Driven\n\n\nThe purpose of the Goal-Driven approach is to enable your multi-agent system (e.g., Claude Code, Codex, OpenClaw) to sustain more than 300 hours of continuous effort in solving an extremely complex problem that has a specific objective and a set of strict, well-defined criteria.\n\nGoal-Driven is best suited for tasks that are highly intricate, time-consuming, logically complex, and abstract—yet can be thoroughly evaluated for success. Typical examples include compiler design, mathematical theorem proving, computational problems, database architecture, sophisticated system-level design, and EDA simulation challenges.\n\nThe core concepts of Goal-Driven are as follows:\n\n1. Goal – the ultimate objective of the entire system and the central task of all subagents.\n\n2. Criteria – a clear set of conditions that allow the master agent to determine whether the task is completed and the goal has been achieved.\n\n3. Subagent – an agent responsible for continuously working toward solving the problem and achieving the assigned goal.\n\n4. Master Agent – the only controller of subagents, responsible for independently evaluating whether the goal has been reached based on the defined criteria. It acts as the final decision-maker for the system’s process.\n\nThe core philosophy of Goal-Driven is straightforward:\n\n1. When the Goal-Driven process starts, the master agent creates a subagent and instructs it to persistently work toward solving the problem and reaching the goal.\n\n2. The master agent periodically checks whether the subagent is active. If the subagent becomes inactive, claims completion, or enters an idle state, the master agent must evaluate the current result according to the criteria. If the result fails to meet the criteria, it commands the subagent to continue working, repeating this cycle until the criteria are satisfied.\n\n3. Once the subagent’s output meets the criteria, the system halts and announces successful completion.\n\nThe pseudocode representation of this process is as follows:\n```\nwhile (criteria not met)\n{\n    let the subagent work on solving the problem and achieving the Goal\n}\n```\n\n## Example of open source projects developed by Goal-Driven system\n\nTypeScript compiler in C++(in ~100 hours) [https:\u002F\u002Fgithub.com\u002Flidangzzz\u002FTypeScript-C-Implementation-by-OnlySpecs](https:\u002F\u002Fgithub.com\u002Flidangzzz\u002FTypeScript-C-Implementation-by-OnlySpecs)\n\nSQLite in Rust(in ~30 hours) [https:\u002F\u002Fgithub.com\u002Flidangzzz\u002Fsqlite-rust-by-OnlySpecs](https:\u002F\u002Fgithub.com\u002Flidangzzz\u002Fsqlite-rust-by-OnlySpecs)\n\nLean4 compiler in TypeScript (in progress) [https:\u002F\u002Fgithub.com\u002Flidangzzz\u002FLean4-ts](https:\u002F\u002Fgithub.com\u002Flidangzzz\u002FLean4-ts)\n\n\n## Usage:\nCopy the following prompt into your text editor. Carefully fill in the blanks for both goal and criteria, then run it using your multi-agent–supported tool (for example, Claude Code, Codex, OpenClaw, etc.).\n\n## Example:\n\nGoal: [[[[[Write a TypeScript compiler in C++ that correctly transpiles TypeScript into JavaScript, including complete documentation and unit tests.]]]]]\n\nCriteria for success: [[[[[Ensure that the TypeScript compiler successfully compiles and generates 2,000 comprehensive TypeScript test case files covering as many TypeScript syntax features as possible. Confirm that the C++ TypeScript compiler correctly transpiles the code into JavaScript. Then, run both the outputs from this compiler and the official tsc transpiler on Node.js, and verify that the two resulting JavaScript files produce identical outputs.]]]]]\n\n\n## Notes:\n\n1. I strongly advise not adding this prompt to any AI agent’s skills or plugins, as doing so could contaminate your context window.\n\n2. Goal-Driven processes may consume significant time and LLM tokens; please make sure your AI agent’s API plan or subscription balance is sufficient.\n\n\n\n-----\n\n# The Goal-Driven Prompt Template (1 master agent + 1 subagent)\n\n```\n# Goal-Driven(1 master agent + 1 subagent) System\n\nHere we define a goal-driven multi-agent system for solving any problem.\n\nGoal: [[[[[DEFINE YOUR GOAL HERE]]]]]\n\nCriteria for success: [[[[[DEFINE YOUR CRITERIA FOR SUCCESS HERE]]]]]\n\nHere is the System: The system contains a master agent and a subagent. You are the master agent, and you need to create 1 subagent to help you complete the task.\n\n## Subagent's description: \n\nThe subagent's goal is to complete the task assigned by the master agent. The goal defined above is the final and the only goal for the subagent. The subagent should have the ability to break down the task into smaller sub-tasks, and assign the sub-tasks to itself or other subagents if necessary. The subagent should also have the ability to monitor the progress of each sub-task and update the master agent accordingly. The subagent should continue to work on the task until the criteria for success are met.\n\n## Master agent's description: \n\nThe master agent is responsible for overseeing the entire process and ensuring that the subagent is working towards the goal. The only 3 tasks that the main agent need to do are: \n\n1. Create subagents to complete the task. \n2. If the subagent finishes the task successfully or fails to complete the task, the master agent should evaluate the result by checking the criteria for success. If the criteria for success are met, the master agent should stop all subagents and end the process. If the criteria for success are not met, the master agent should ask the subagent to continue working on the task until the criteria for success are met.\n3. The master agent should check the activities of each subagent for every 5 minutes, and if the subagent is inactive, please check if the current goal is reached and verify the status. If the goal is not reached, restart a new subagent with the same name to replace the inactive subagent. The new subagent should continue to work on the task and update the master agent accordingly.\n4. This process should continue until the criteria for success are met. DO NOT STOP THE AGENTS UNTIL THE USER STOPS THEM MANUALLY FROM OUTSIDE.\n\n## Basic design of the goal-driven double agent system in pseudocode:\n\ncreate a subagent to complete the goal\n\nwhile (criteria are not met) {\n  check the activty of the subagent every 5 minutes\n  if (the subagent is inactive or declares that it has reached the goal) {\n    check if the current goal is reached and verify the status\n    if (criteria are not met) {\n      restart a new subagent with the same name to replace the inactive subagent\n    } \n    else {\n      stop all subagents and end the process\n    }\n  }\n}\n```\n","[中文版](readme_cn.md)\n\n# 目标驱动\n\n目标驱动方法的目的是使您的多智能体系统（例如 Claude Code、Codex、OpenClaw）能够持续超过300小时，不断努力解决一个具有明确目标和一组严格、清晰定义的标准的极其复杂的问题。\n\n目标驱动最适合那些高度复杂、耗时、逻辑性强且抽象，但又可以被彻底评估是否成功的任务。典型的例子包括编译器设计、数学定理证明、计算问题、数据库架构、复杂的系统级设计以及 EDA 仿真挑战。\n\n目标驱动的核心概念如下：\n\n1. 目标——整个系统的最终目标，也是所有子智能体的核心任务。\n2. 标准——一套明确的条件，使主智能体能够判断任务是否完成以及目标是否达成。\n3. 子智能体——负责持续工作以解决问题并实现所分配目标的智能体。\n4. 主智能体——唯一控制子智能体的实体，负责根据定义的标准独立评估目标是否已达成。它是系统流程的最终决策者。\n\n目标驱动的核心理念非常简单：\n\n1. 当目标驱动流程启动时，主智能体将创建一个子智能体，并指示其持续努力解决问题并达成目标。\n2. 主智能体会定期检查子智能体是否处于活跃状态。如果子智能体停止活动、声称已完成任务或进入空闲状态，主智能体必须根据标准评估当前结果。如果结果不符合标准，则命令子智能体继续工作，重复这一循环，直到满足标准为止。\n3. 一旦子智能体的输出符合标准，系统就会停止并宣布成功完成。\n\n该过程的伪代码表示如下：\n```\nwhile (criteria not met)\n{\n    let the subagent work on solving the problem and achieving the Goal\n}\n```\n\n## 目标驱动系统开发的开源项目示例\n\n用 C++ 实现的 TypeScript 编译器（约 100 小时）[https:\u002F\u002Fgithub.com\u002Flidangzzz\u002FTypeScript-C-Implementation-by-OnlySpecs](https:\u002F\u002Fgithub.com\u002Flidangzzz\u002FTypeScript-C-Implementation-by-OnlySpecs)\n\n用 Rust 实现的 SQLite（约 30 小时）[https:\u002F\u002Fgithub.com\u002Flidangzzz\u002Fsqlite-rust-by-OnlySpecs](https:\u002F\u002Fgithub.com\u002Flidangzzz\u002Fsqlite-rust-by-OnlySpecs)\n\n用 TypeScript 实现的 Lean4 编译器（进行中）[https:\u002F\u002Fgithub.com\u002Flidangzzz\u002FLean4-ts](https:\u002F\u002Fgithub.com\u002Flidangzzz\u002FLean4-ts)\n\n\n## 使用方法：\n将以下提示复制到您的文本编辑器中。仔细填写目标和标准两处的空白，然后使用支持多智能体的工具（例如 Claude Code、Codex、OpenClaw 等）运行它。\n\n## 示例：\n\n目标：[[[[[用 C++ 编写一个能够正确将 TypeScript 转换为 JavaScript 的 TypeScript 编译器，包含完整的文档和单元测试。]]]]]\n\n成功标准：[[[[[确保该 TypeScript 编译器能够成功编译并生成 2,000 个覆盖尽可能多 TypeScript 语法特性的全面测试用例文件。确认 C++ 版本的 TypeScript 编译器能正确地将代码转译成 JavaScript。随后，在 Node.js 上分别运行该编译器和官方 tsc 编译器的输出，验证两者生成的 JavaScript 文件是否完全一致。]]]]]\n\n\n## 注意事项：\n\n1. 强烈建议不要将此提示添加到任何 AI 智能体的技能或插件中，因为这可能会污染您的上下文窗口。\n2. 目标驱动流程可能会消耗大量时间和 LLM 令牌，请确保您的 AI 智能体 API 计划或订阅余额充足。\n\n\n\n-----\n\n# 目标驱动提示模板（1 个主智能体 + 1 个子智能体）\n\n```\n# 目标驱动（1 个主智能体 + 1 个子智能体）系统\n\n这里我们定义了一个用于解决任何问题的目标驱动多智能体系统。\n\n目标：[[[[[在此处定义您的目标]]]]]\n\n成功标准：[[[[[在此处定义您的成功标准]]]]]\n\n系统构成如下：系统包含一个主智能体和一个子智能体。您是主智能体，需要创建 1 个子智能体来帮助您完成任务。\n\n## 子智能体描述：\n\n子智能体的目标是完成主智能体分配的任务。上述定义的目标是子智能体的最终且唯一的目标。子智能体应具备将任务分解为更小子任务的能力，并在必要时将这些子任务分配给自己或其他子智能体。此外，子智能体还应能够监控每个子任务的进展，并及时向主智能体汇报。子智能体将持续工作，直至满足成功标准。\n\n## 主智能体描述：\n\n主智能体负责监督整个过程，确保子智能体朝着目标前进。主智能体需要执行的三项任务是：\n\n1. 创建子智能体以完成任务。\n2. 如果子智能体成功完成任务或未能完成任务，主智能体应通过检查成功标准来评估结果。若满足成功标准，主智能体应停止所有子智能体并结束流程。若未满足成功标准，则应要求子智能体继续工作，直至达到成功标准。\n3. 主智能体应每 5 分钟检查一次各子智能体的活动情况。若发现子智能体不活跃，请检查当前目标是否已达成，并核实状态。若目标尚未达成，则需重新启动一个同名子智能体来替换不活跃的子智能体。新子智能体将继续执行任务，并及时向主智能体汇报进展。\n4. 此过程将持续进行，直到满足成功标准为止。请勿在用户从外部手动停止之前停止智能体的工作。\n\n## 目标驱动双智能体系统的基本设计（伪代码）：\n\n创建一个子智能体以完成目标\n\nwhile (criteria are not met) {\n  check the activty of the subagent every 5 minutes\n  if (the subagent is inactive or declares that it has reached the goal) {\n    check if the current goal is reached and verify the status\n    if (criteria are not met) {\n      restart a new subagent with the same name to replace the inactive subagent\n    } \n    else {\n      stop all subagents and end the process\n    }\n  }\n}\n```","# Goal-Driven 快速上手指南\n\nGoal-Driven 是一种提示词工程模式，旨在让多智能体系统（如 Claude Code、Codex、OpenClaw 等）能够持续工作数百小时，以解决具有明确目标和严格验收标准的极度复杂问题（如编译器设计、数学证明、系统架构等）。\n\n该工具无需安装软件包，其核心是一个精心设计的**提示词模板（Prompt Template）**。\n\n## 环境准备\n\n*   **多智能体支持工具**：你需要拥有一个支持多智能体协作或长上下文运行的 AI 编程工具。\n    *   推荐工具：Claude Code, Codex, OpenClaw 等。\n*   **账号与额度**：\n    *   确保你的 API 套餐或订阅余额充足。Goal-Driven 过程可能消耗大量 Token 并运行较长时间。\n*   **编辑器**：任意文本编辑器，用于编辑和复制提示词。\n\n> **注意**：请勿将此提示词添加为 AI 代理的固定技能或插件，以免污染上下文窗口。应将其作为一次性任务指令输入。\n\n## 安装步骤\n\n本工具无需执行安装命令。请按以下步骤操作：\n\n1.  打开你的文本编辑器。\n2.  复制下方的 **[Goal-Driven 提示词模板](#goal-driven-提示词模板)**。\n3.  根据你的具体需求，填写模板中的 `Goal`（目标）和 `Criteria for success`（成功标准）部分。\n4.  将填充完整的提示词粘贴到你的多智能体工具对话框中并运行。\n\n## 基本使用\n\n### 1. 核心概念\n在使用前，请明确以下两个关键要素：\n*   **Goal (目标)**：系统的终极目标，也是子代理（Subagent）的唯一任务。\n*   **Criteria (标准)**：一组清晰的条件，主代理（Master Agent）据此判断任务是否完成。只有满足这些标准，系统才会停止。\n\n### 2. 使用示例\n假设你要构建一个 C++ 版的 TypeScript 编译器。\n\n**步骤一：定义目标与标准**\n*   **Goal**: `Write a TypeScript compiler in C++ that correctly transpiles TypeScript into JavaScript, including complete documentation and unit tests.`\n*   **Criteria**: `Ensure that the TypeScript compiler successfully compiles and generates 2,000 comprehensive TypeScript test case files covering as many TypeScript syntax features as possible. Confirm that the C++ TypeScript compiler correctly transpiles the code into JavaScript. Then, run both the outputs from this compiler and the official tsc transpiler on Node.js, and verify that the two resulting JavaScript files produce identical outputs.`\n\n**步骤二：填入模板并运行**\n将上述内容填入下方模板的对应位置，然后发送给 AI 工具。\n\n### Goal-Driven 提示词模板\n\n```markdown\n# Goal-Driven(1 master agent + 1 subagent) System\n\nHere we define a goal-driven multi-agent system for solving any problem.\n\nGoal: [[[[[DEFINE YOUR GOAL HERE]]]]]\n\nCriteria for success: [[[[[DEFINE YOUR CRITERIA FOR SUCCESS HERE]]]]]\n\nHere is the System: The system contains a master agent and a subagent. You are the master agent, and you need to create 1 subagent to help you complete the task.\n\n## Subagent's description: \n\nThe subagent's goal is to complete the task assigned by the master agent. The goal defined above is the final and the only goal for the subagent. The subagent should have the ability to break down the task into smaller sub-tasks, and assign the sub-tasks to itself or other subagents if necessary. The subagent should also have the ability to monitor the progress of each sub-task and update the master agent accordingly. The subagent should continue to work on the task until the criteria for success are met.\n\n## Master agent's description: \n\nThe master agent is responsible for overseeing the entire process and ensuring that the subagent is working towards the goal. The only 3 tasks that the main agent need to do are: \n\n1. Create subagents to complete the task. \n2. If the subagent finishes the task successfully or fails to complete the task, the master agent should evaluate the result by checking the criteria for success. If the criteria for success are met, the master agent should stop all subagents and end the process. If the criteria for success are not met, the master agent should ask the subagent to continue working on the task until the criteria for success are met.\n3. The master agent should check the activities of each subagent for every 5 minutes, and if the subagent is inactive, please check if the current goal is reached and verify the status. If the goal is not reached, restart a new subagent with the same name to replace the inactive subagent. The new subagent should continue to work on the task and update the master agent accordingly.\n4. This process should continue until the criteria for success are met. DO NOT STOP THE AGENTS UNTIL THE USER STOPS THEM MANUALLY FROM OUTSIDE.\n\n## Basic design of the goal-driven double agent system in pseudocode:\n\ncreate a subagent to complete the goal\n\nwhile (criteria are not met) {\n  check the activty of the subagent every 5 minutes\n  if (the subagent is inactive or declares that it has reached the goal) {\n    check if the current goal is reached and verify the status\n    if (criteria are not met) {\n      restart a new subagent with the same name to replace the inactive subagent\n    } \n    else {\n      stop all subagents and end the process\n    }\n  }\n}\n```\n\n### 3. 运行监控\n*   启动后，主代理会自动创建子代理并开始工作。\n*   主代理会每 5 分钟检查一次子代理状态。如果子代理停滞或声称完成但未达标，主代理会重启子代理继续工作。\n*   **切勿手动中断**：除非你决定放弃任务，否则不要手动停止代理，直到系统自动宣布满足成功标准并结束流程。","某金融科技团队需要在 48 小时内从零构建一个符合严格数学验证标准的高频交易撮合引擎，该引擎需处理复杂的订单逻辑并确保零误差。\n\n### 没有 goal-driven 时\n- **任务中途夭折**：普通多智能体系统在运行数小时后，常因遇到逻辑死胡同或产生幻觉而自行停止，无法完成长达数十小时的连续编码任务。\n- **验收标准模糊**：缺乏统一的“主智能体”进行最终裁决，子任务往往在功能未完全达标时就声称完成，导致交付物无法通过严格的压力测试。\n- **迭代效率低下**：开发者需要人工反复检查进度并重新下达指令，一旦离开视线，系统便陷入空闲状态，无法实现真正的无人值守开发。\n- **复杂逻辑断裂**：面对撮合算法中深层的数学证明和边界条件，单一会话上下文容易丢失核心目标，导致代码逻辑前后不一致。\n\n### 使用 goal-driven 后\n- **超长持续运行**：goal-driven 通过主智能体监控机制，强制子智能体在未达到标准前持续工作，成功支撑了超过 100 小时的连续攻关，直至引擎完工。\n- **严格结果导向**：预设的“成功标准”（如通过 5000 组极端行情回测且资金零误差）成为唯一停止信号，确保交付即达标，杜绝了半成品流出。\n- **全自动闭环迭代**：主智能体自动检测子智能体状态，一旦发现停滞或未完成，立即指令其继续修正，实现了从设计到验证的全程自动化闭环。\n- **目标高度聚焦**：无论中间过程多么曲折，所有子任务始终围绕“构建零误差撮合引擎”这一终极目标展开，保证了复杂系统架构的逻辑一致性。\n\ngoal-driven 的核心价值在于将原本碎片化、易中断的 AI 辅助编程，转化为能够独立攻克高难度、长周期工程难题的自主研发实体。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flidangzzz_goal-driven_c5603e4a.png","lidangzzz","立党 Lidang","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Flidangzzz_e41f5d48.png","Creator of Hedgehog Lab and Hedgehog Computing Group\r\n","@Hedgehog-Computing ","Texas",null,"linktr.ee\u002Flidang","https:\u002F\u002Fgithub.com\u002Flidangzzz",696,44,"2026-04-10T05:49:38",1,"","未说明",{"notes":89,"python":87,"dependencies":90},"该工具并非传统软件，而是一套用于多智能体系统（如 Claude Code, Codex, OpenClaw）的提示词模板和方法论。因此没有特定的操作系统、GPU、内存或编程语言版本要求。运行环境完全取决于用户所使用的外部多智能体工具及其 API 服务。主要成本在于 LLM Token 消耗和时间（任务可能持续数百小时），需确保订阅额度充足。切勿将此提示词添加为 AI 代理的技能或插件，以免污染上下文窗口。",[],[13],"2026-03-27T02:49:30.150509","2026-04-10T20:45:45.761945",[],[]]